AI ‘Pacing’ Doesn’t Mean Slower Adoption
TL;DR
NEA partner Tiffany Luck says any slowdown in development at the AI frontier doesn’t have to slow adoption across the economy. She argues increasingly powerful models from OpenAI, Anthropic and others are already well ahead of enterprise deployment, leaving a major opportunity for companies that can turn raw intelligence into real-world workflows and measurable returns. She joins Ed Ludlow on "Bloomberg Tech. " (Source: Bloomberg).
Nauti's Take
Small teams should start with one concrete workflow and measure time saved, error rates, and the amount of human review required. If the results fall short of the promise, the constraint is likely to be data, integration, or ownership rather than a lack of model capability.
Summary
NEA partner Tiffany Luck says any slowdown in development at the AI frontier doesn’t have to slow adoption across the economy. She argues increasingly powerful models from OpenAI, Anthropic and others are already well ahead of enterprise deployment, leaving a major opportunity for companies that can turn raw intelligence into real-world workflows and measurable returns.
She joins Ed Ludlow on "Bloomberg Tech. " (Source: Bloomberg)
Sources